概率神经网络在物质浓度辨识中的应用

Application of probabilistic neural network in material concentration recognition

提出了基于概率神经网络的物质浓度辨识方法,以二氧化硫在不同浓度下颜色读数的数据为例,建立概率神经网络的物质浓度辨识模型。实验仿真表明,概率神经网络物质浓度辨识模型具有收敛速度快、物质浓度辨识正确率高、容易训练等特点。

In this paper, a method of material concentration recognition based on probabilistic neural network is proposed. Take the color reading of sulfur dioxide at different concentrations as an example, establishing a material concentration recognition model of probabilistic neural network. Through experimental simulation shows, PNN material concentration recognition model have features like fast convergence, material concentration recognition high accuracy rate, easy to train, and so on.